risk的用法5篇
risk的用法(1)
The Effect of Systematic Credit Risk on Loan Portfolio
Value at Risk and on Loan Pricing
Submitted to the JP Morgan CreditMetrics Monitor
Dr. Barry Belkin, Daniel H. Wagner Associates
Dr. Lawrence R. Forest, Jr., KPMG
Using a contingent claims model framework, CreditMetricsTM derives correlations between ratings migrations of different borrowers from the observed correlations between equity values of the different industries and countries of those borrowers. As a variation of that analysis, we treat correlations here as arising from a single systematic risk factor. Applying this single-factor model, in Section I we evaluate the effect of systematic credit risk on loan portfolio value at risk. In Section II we focus on individual loan transactions and assess the effect of systematic risk on credit spreads and therefore loan pricing and valuation decisions. We attach great importance to this effect, because arbitrage theory provides the basis for the Loan Analysis SystemSM (LAS) developed by KPMG Peat Marwick to support commercial loan valuation and pricing. Our results show that one must properly account for systematic factors separately from specific factors if one is to assess risk accurately at both the loan portfolio and loan transaction level.
I. Systematic Credit Risk at the Loan Portfolio Level
In this Section we evaluate the effect of systematic credit risk on loan portfolio value at risk. We compare the approach that considers only two credit states (default and no-default) with the one that distinguishes among multiple non-default states (the full-state model). In addition, we compare the portfolio payoff distribution under the full-state model with the corresponding Gaussian distribution that would obtain if all borrowers were uncorrelated. Not surprisingly, we find that the Gaussian distribution poorly approximates actual value at risk. The two-state model provides a better estimate, though we still observe important discrepancies.
To model credit-risk correlation, we follow the CreditMetricsTM approach described in [1] and assume that transitions in discrete ratings grades occur as a result of migration in an underlying process that measures “distance” from default. To simplify the presentation, we work with default distance transformed into a normalized risk score whose one-period changes have a unit normal distribution.
We index borrowers by i and let denote the one-period change in the normalized risk score of borrower . To represent correlation in a straightforward way, we assume that we can write each as:
(1)
Here Z represents a unit normal random variable measuring the composite effect of economic factors influencing the rating-score migration of all borrowers. We refer to this systematic component of credit risk as “Z-risk.” The represent mutually independent unit normal variables (also independent of Z), each specific to an individual borrower. We refer to the credit risk induced by each as “-risk.” The parameter determines the fraction of the variance of that is attributable to Z-risk. The correlation between and for any is
Since an investor can eliminate -risk through diversification, it commands no risk premium. Z-risk, on the other hand, appears in every portfolio of loans and in broad-based portfolios of other assets, no matter how varied. Since Z-risk cannot be diversified away, it accounts for the risk premium or “unexpected” credit loss charged on loans.
In what follows, we analyze the typical way in which Z-risk and credit correlation affect portfolio-wide risk. To simplify the analysis, we consider a loan portfolio in which each borrower has the same exposure to Z-risk. We thus replace the borrower specific by a single value common to all borrowers. The model in Equation (1) then reduces to:
(2)
The correlation between any pair of borrowers is now . The two extreme cases are: (i) -risk only () and (ii) Z-risk only ().
We define ratings by partitioning the into disjoint bins defined by boundary values . We fix the so that the one-period ratings transition probabilities calculated from (2) agree with observed historical rating migration probabilities. This method of calibrating the model (2) results in a separate set of ratings grade breakpoints for each starting grade. This conflicts with the view that represents a simple transform of default distance. Also, by tying default to the position of at the end of the analysis period only, we depart from the view that default represents a trapping state in continuous time. Despite these limitations, the model has proved useful as a starting point in evaluating credit correlation.
Consider a two-period simple (option-free) term loan L. Let X denote the payoff to the lender at the end of the first period. We apply the standard variance decomposition formula to X:
. (3)
The term represents the average amount of payoff variability induced by It measures (diversifiable) -risk. The term measures the systematic variability in loan payoff induced by Z-risk.
Consider a loan portfolio that includes a large number N of L-type loans made to different borrowers, each with the same initial rating. Let SN denote the payoff of the portfolio . The payoffs of the loans in are conditionally independent given Z. It follows, therefore, that
(4)
The systematic term in the variance decomposition of scales with , while the diversifiable term scales with . Thus, given positive correlation among the ratings migrations of different borrowers, for large enough the systematic risk will dominate in the variance decomposition
(5)
For independent identically distributed variables, the central limit theorem applies to the partial sums , with a normalization that scales with . In the present circumstance, we see that if any limit law holds, the normalization must scale with . We now ask whether any counterpart to the central limit theorem holds.
To answer this question, we created a spreadsheet that calculates the probability distribution for the payoff from a portfolio of two-period loans. We set and determined the distribution for the portfolio’s net present value. We also looked at the “normalized net present value,” meaning the deviation from the portfolio’s mean NPV divided by . This represents the unexpected portfolio gain or loss measured in units of standard deviation. We use this normalized variable to quantify portfolio value at risk.
We analyzed a two-year loan under the following assumptions:
? A rating system with seven non-default rating grades (Aaa, Aa, A, Baa, Ba, B, and Caa)
? One-year rating grade transition matrix taken from Moody’s Report (see [4])
? Constant risk-free interest rate = 4.5%
? No loan origination or holding cost
? Fixed loss in the event of default (LIED) =
? Credit risk premiums (unexpected loss) with a flat forward term structure: Aaa: 3.0 bps, Aa: 4.8 bps, A: 10.0 bps, Baa: 16.0 bps, Ba: 120.0 bps, B: 150.0 bps, Caa: 300.0 bps
? One of two values for credit migration correlation: .
We obtain a highly skewed value-at-risk distribution with long lower tail (see Figure 1). We illustrate this by examining the limit density for the normalized portfolio value at risk in the case where (i) each borrower is rated Ba at loan origination, (ii) the correlation parameter has the value .25, and (iii) each loan has a coupon of LIBOR + 171.9 bps (which represents par at origination). In determining , we translated the portfolio payoff by its mean value and then scaled the result by the portfolio payoff standard deviation .
For comparison, we have also displayed the limit density that results if one assigns a two-period loan the value $1 (par value) at the end of period 1 if that loan does not default during period 1 and the value if the loan defaults (see again Figure 1). This collapses the migration process to two states: default and non-default. Under this default/no default model, the mean portfolio payoff is $10,116.25, quite close to the value of $10,116.10 obtained under the full-state model. However, the portfolio payoff standard deviation is $83.26, substantially below the $96.00 value for the full-state model.
Finally, we show the density , the unit Gaussian density that would obtain for normalized value at risk if the migration processes of the borrowers were statistically independent. In that case, the central limit theorem would apply unconditionally and the density for the normalized portfolio payoff would closely resemble the unit normal.
Both and are markedly asymmetric and have modes that are skewed to the right. The mode of is more narrowly peaked than that of . The upper tail of falls off smoothly, while that of is sharply truncated. The lower tails of the two densities differ in ways not discernible in the diagram. Consider, for example, the probability of a portfolio loss. The expected portfolio profit of $116.10 corresponds to . Thus, portfolio losses occur if the payoff falls short of the mean by more than . Under such an outcome occurs with probability .080. The corresponding probability under is .065. So if we were to use to approximate , we would understate the probability of a portfolio loss.
We of course observe a much more striking disparity between and the unit normal density . The unit normal density ignores systematic risk, so it comes as no surprise that it poorly approximates . The density is asymmetric, whereas the Gaussian density is symmetric. The density has a heavier lower tail. Under , a portfolio loss occurs with probability .113. Thus, if we were to use the Gaussian as a proxy for , we would overestimate the probability of a loss.
To summarize, our results show that if one doesn’t distinguish among different non-default grades or especially if one doesn’t account for systematic credit risk, one will make important errors in estimating portfolio value-at-risk.
Figure 1
II. Systematic Credit Risk at the Loan Transaction Level
In modeling credit risk, most analysts work with an ordered set of several non-default states as well as a single trapping default state. This multinomial framework creates a dilemma. One would like to apply arbitrage-free methods in pricing for credit risk. However, in a finite (discrete time and discrete risk rating) model, these methods have found success in uniquely identifying prices only for binomial credit risks. “Binomial” refers to a model with only two credit states -- default and non-default.
In [3] the authors resolve this dilemma by introducing one-period binomial reference loans with payoffs that approximate the one-period payoffs of the actual multinomial loan. Specifically, they construct binomial loans with payoff means and variances that match those of the one-period payoffs of the multinomial loan. For reasons that will be made clear below, we refer to this calibration as the total risk method. Each reference loan has only two possible payoff values. Thus, each one can be priced uniquely by arbitrage. The authors then outline a recursive procedure for computing the value of the actual multiperiod multinomial loan from the associated values of the binomial reference loans.
The authors in [3] consider a portfolio of statistically identical multinomial loans (each with the same multinomial payoff distribution) and a portfolio of statistically identical binomial loans whose payoffs are those of the associated reference loans. They then argue that the value of portfolio and the value of portfolio must approach equality as . The argument relies on the central limit theorem.
However, the needed assumption of statistical independence is problematic. If one could construct arbitrarily large portfolios of independent but statistically identical loans, the credit risks being priced would be fully diversifiable and would command no market premium. This conflicts with the observation that loan spreads in the market include a component for unexpected as well as expected credit loss. The market therefore indicates that the ratings migration processes of borrowers reflect systematic credit risk.
This raises the question of how one should modify the calibration of the reference loan so as to account for systematic credit risk. The variance decomposition in Equation (2) above provides the answer. We observe that only systematic risk commands a risk premium. Therefore, for two credit risks to be priced the same by the market, they should have the same amount of systematic risk. Thus the calibration in [3] more properly would involve matching the systematic variance of the reference loan payoffs to the systematic variance of the multinomial loan payoffs. We refer to this scheme as the systematic risk method.
Alternatively, we could structure the binomial loan payoffs so that they created the least amount of systematic basis risk relative to the multinomial loan. In other worlds, we would structure the binomial loan to minimize the following expression:
, (6)
in which denotes the year-1 payoff to the binomial loan. This basis risk minimization method turns out to be mathematically equivalent to the systematic risk method if the conditional expectation of the binomial loan payoff and the conditional expectation of the multinomial loan payoff have a correlation coefficient of unity. For the relevant range of values of the problem parameters, we have observed this correlation to be .99 or higher. Furthermore, we have separately applied the systematic risk method and the basis risk minimization method and observed that the two schemes produce nearly identical loan values and par spreads.
We have created a spreadsheet that applies both the total risk method and the alternative systematic risk method. In this spreadsheet, we have represented the systematic risk variable Z using 1,000 equiprobability bins. For each discrete value of Z representing a bin, we determine the conditional moments for the payoff distribution both of an individual loan and a portfolio of 10,000 loans statistically identical to the given loan. In these calculations, we make strong use of the conditional independence of the portfolio loan payoffs for each value of Z.
We applied the total variance decomposition to the previously described two-period loan with the borrower initially in rating grade Ba, a (par) credit spread of 171.9 bps, and . The results indicate that, for an individual loan, most of the payoff variance (about 95%) reflects diversifiable risk (see Table 1). If we use the total risk calibration scheme in fashioning the binomial reference loan, we find that its payoff distribution understates the systematic variance of actual loan payoffs.
At the portfolio level, virtually all of the payoff variance (99.7%) derives from systematic risk (see Table 2). Furthermore, we now see a substantial gap between the payoff variances for the binomial- and multinomial-loan portfolios. Consequently, the second-order match enforced for individual transactions does not carry over to the portfolio.
Table 1
Variance Decomposition: Individual Loan
Table 2
Variance Decomposition: Loan Portfolio
These results show that the small systematic component of risk for individual loans almost entirely determines credit risk for large portfolios of loans. Risk decomposition therefore becomes an essential element in loan valuation and pricing.
To provide further insight, we examine the (normalized) portfolio payoff densities for loans with an initial risk grade of Ba and with credit migration correlations of and , respectively. We first review the match between the multinomial and binomial reference loan portfolios for the case case (see Figures 2 and 3) and then the case (see Figures 4 and 5). In each instance, we initially examine the accuracy of the reference loan approximations for the total risk method of calibration and then for the systematic risk method of calibration.
These comparisons are motivated by the principle (advanced in [3]) that the closer the portfolios and are in their payoff distributions, the closer the common price of the loans in should be to the common price of the loans in . We see that the agreement between the payoff distributions for the portfolios and is closer (but still not exact) if the calibration is based on systematic risk as opposed to total risk.
We observe this improvement both when and when . In each case, the locations of the density peaks become better aligned and the disparity in peak height diminishes somewhat. Using the systematic risk method, the variances of the portfolio payoffs for and agree very closely. We get much less agreement when we use the total risk method. Finally, we obtain a closer match between the lower tails of the two densities using the systematic risk method, although that is not apparent at the scale at which the densities are plotted.
Figure 2
Figure 3
Figure 4
Figure 5
One concludes that credit risks in the finite model context cannot in general be priced exactly by arbitrage methods using binomial reference loans. Nonetheless, the accuracy of the reference loan technique in [3] can be improved if the systematic risk calibration method is used in place of the total risk method.
To quantify the effect of the reference loan calibration method on loan pricing, we calculated the par credit spreads for the two-period loans under consider-ation. We determined these par spreads first using the total risk method and then using the systematic risk method (see Table 3 for the results when ).
Table 3
Effect of Reference Loan Calibration Method on Par Credit Spreads
Borrower Initial Risk Rating
The par spreads are consistently higher under the systematic risk calibration method. This reflects our earlier observation that, using the total risk method, we underestimate the systematic component of loan payoff variance. Matching the systematic component of loan variance therefore causes the reference loan to appear more risky. This lowers the loan value to the lender and increases the required par credit spreads. The pricing differences are largest in relative terms at the higher rating grades and largest in absolute terms at the lower rating grades.
Incorporating the systematic risk method into the recursive valuation adds somewhat to the required computation. To implementing the scheme we must:
(i) Calculate the conditional one-period rating transition probabilities given each (discrete) level z of Z-risk.
(ii) Determine the systematic component of variance associated with the possible rating transitions out of each node .
(iii) Calibrate the reference loan payoffs (the non-default payoff) and (the default payoff) at each node to match the mean and systematic variance of the binomial loan payoff to the mean and systematic variance of the corresponding multinomial loan payoff
To perform these calculations, we must first estimate the correlation parameter . Under the CreditMetricsTM approach, one treats rating migration as driven by asset value movement relative to a default threshold. One may then estimate individual obligor correlations from industry and country asset correlations and the associated participation weights. One can then construct a well-diversified portfolio as a proxy for the market portfolio and define the market portfolio to be the weighted average of the appropriate .
In closing, we note that arbitrage theory provides the basis for the Loan Analysis SystemSM (LAS) developed by KPMG Peat Marwick to support commercial loan valuation and pricing. The desire to incorporate into LAS improved methods for the arbitrage pricing of credit risk motivated the work described in this article.
Summary. Systematic credit risk accounts for the observation that credit risk can not “diversified away” even in large loan portfolios. To quantify the effects of systematic credit risk, we have postulated a one-factor model for credit rating migration that separates risk common to all borrowers from risk which is independent from borrower to borrower and therefore diversifiable.
Systematic risk causes the loan portfolio value-at-risk distribution to take on a distinctly non-Gaussian character. Relative to the unit Gaussian density, the mode of the (normalized) value-at-risk density is much shifted to the right and the lower tail is substantially elongated.
We also explored the effect of accounting for systematic risk but only distinguishing between default and non-default in credit rating migration. Our results indicate that this simplification also affects the overall shape of the value-at-risk density. The (normalized) value-at-risk density now has an exagerated mode and a sharply truncated upper tail. This binomial approach also creates inaccuracy in determining the portfolio loss distribution quantiles.
Finally, we investigated the impact of systematic risk on arbitrage-free loan pricing. We observed that if one fails to distinguish between systematic and diversifiable risk in applying arbitrage methods, this will create pricing error. We then described the systematic risk method as an improved (but necessarily still approximate) technique for arbitrage pricing. It is characteristic of the systematic risk method that the value of a loan depends on the fraction of obligor credit risk that is systematic.
The views and opinions are those of the author and do no necessarily represent the views and opinions of KPMG Peat Marwick LLP.
References
[1] “CreditMetricsTM”, J. P. Morgan Report, April 1997
[2] “Measuring Changes in Corporate Credit Quality,” Moody’s Special Report, November 1993
[3] “Debt Rating Migration and the Valuation of Commercial Loans,” A. Ginzburg, K. J. Maloney, and R. Willner, Citibank Portfolio Strategies Group Report, December 1994
[4] “Moody’s Ratings Migration and Credit Quality Correlations, 1920-1996,” Moody’s Report, 1997
[5] “Martingales and Stochastic Integrals in the Theory of Continuous Trading,” J. M. Harrison and S. R. Pliska, Stochastic Processes and Their Applications, 11, 1981, pp. 215-260
risk的用法(2)
at the risk ofHe saved my life?at the risk of?his own.??
他冒著自己的生命危險救了我的命。
2
At the risk of?sounding ungrateful, I must refuse your offer.??
我甘受拂逆盛情之嫌，也必須謝絕你的提議。
3
Sometimes he went after these herbs?at the risk of?his life.??
有時他得冒生命危險來找這些草藥。
4
He said it?at the risk of?losing his job.??
他說這個冒失業的危險。
5
He says it?at the risk of?lose his job.??
他說這個冒失業的危險。
6
He saved my life?at the risk of?losing his own.??
他冒著生命危險救了我。
7
He rescued a child?at the risk of?his own life.??
他冒著生命危險把那個孩子救出來了。
8
We do this?at the risk of?our lives and liberties.??
我們冒著生命及自由的危險做這件事。
9
They were determined to get there even?at the risk of?their life.??
即使要冒生命危險，他們也決心要到那里去。
10
He was determined to do it even?at the risk of?being ridiculed.??
他決定即使冒著被人嘲笑的危險也要做它。
risk的用法(3)
risk的用法與搭配_用法辨析
1. 用作動詞時，為及物動詞，表示冒的危險使遭受危險。如：
They risk their lives in order that we may live more safely. 他們冒了生死危險使我們生活得更安全。
后接動詞作賓語時，要用動名詞，不能用不定式。如：
They risked losing everything. 他們冒失去一切的危險。
A lot of people run the risk of being killed. 許多人冒著被殺害的危險。
2. 用作名詞時，注意以下用法：
(1) 要表示冒危險，通常與動詞run, take等搭配。如：
Why run the risk? 為什么要冒這種危險?
There was so much to lose (that) we couldnt take any risks. 損失這么多，我們不能冒任何風險了。
(2) 表示的危險，其后通常不接不定式，而接 of doing sth。如：
He dare not run [take] the risk of being caught by the police. 他不敢冒被警察抓住的危險。
Theres not much risk of losing money if you bet on that horse. 如果你押那匹馬，輸錢的風險不太大。
比較run [take] the risk of doing sth與run [take] a risk in doing sth：前者指冒做某事的危險或風險，其中的of doing sth為修飾the risk的定語；后者指做某事有危險或冒風險，其中的in doing sth是狀語，in有類似when的含義。如：
I took a risk in telling him the news at that moment. 我那時把消息告訴他，那是冒了風險的。
(3) 注意以下習語的用法：
①at risk 處境危險，在危險中
How much at risk is the smoker? 吸煙者的危險性有多大?
②at ones own risk 對發生的事負責，自擔風險
Persons swimming beyond this point do so at their own risk. 游泳者超越此界限若有意外后果自負。
③at the risk of 冒的危險
He saved my life at the risk of losing his own. 他冒著生命危險救了我的性命。
risk的用法(4)
risk的用法和辨析
今天給大家帶來risk的用法,我們一起來學習吧，下面就和大家分享，來欣賞一下吧。
詞匯精選：risk的用法和辨析
一、詳細釋義：
n.
危險，風險(+of) [U,C]
例句：
Was she not stupid to take this risk?
她冒這樣的風險豈不是太愚蠢了?
例句：
Sometimes he went after these herbs at the risk of his life.
有時他得冒生命危險來找這些草藥。
(保險業承擔的)險;危險率;保險金額;保險對象 [C]
例句：
A group of firms may from a syndicate to pool the risk and assure successful distribution of the issue.
這樣的集團公司可能來自一個財團，實行風險共擔，確保股票成功發行。
貸款對象;賒銷對象 [C]
例句：
Before providing the cash, they will have to decide whether you are a good or bad risk.
在提供資金之前，他們得確認你是不是信譽良好的貸款對象。
v.
冒...的風險(+v-ing)，使遭受危險，以...作為賭注 [T]
例句：
Dont risk your health.
不要拿你的健康冒險。
例句：
The boatman was willing to risk ferrying them across.
船夫愿冒險渡他們過江。
冒險干(+v-ing) [T]
例句：
Dont risk disassembling the machine yourself.
不要冒險自己去拆卸機器。
二、詞義辨析：
danger,risk,hazard,menace,peril,threat
這些名詞均含有“危險、威脅”之意。 danger含義廣，普通用詞，指能夠造成傷害、損害或不利的任何情況。 risk指有可能發生的危險，尤指主動進行某種活動或去碰運氣而冒的危險。 hazard比risk正式，多指偶然發生的或無法控制的危險，常含較嚴重或有一定風險的意味。 menace所指的危險性最嚴重，表示使用暴力或造成破壞性的可能。 peril指迫在眉睫很有可能發生的嚴重危險。 threat普通用詞，語氣弱于menace，指任何公開侵犯對方的言行，給對方構成危險或威脅。
三、詞義辨析：
venture,chance,dare,hazard,risk
這些動詞均含有“敢于冒險”之意。 venture指冒風險試一試，或指有禮貌的反抗或反對。 chance指碰運氣、冒風險試試。 dare可與venture換用，但語氣較強，著重挑戰或違抗。 hazard主要指冒險作出某個選擇，隱含碰運氣意味。 risk指不顧個人安危去干某事，側重主動承擔風險。
四、相關短語：
risk capital
n.[經]風險資本(指為投機性商業投資提供的資金)
run a risk
冒險,擔風險
risk arbitrage
風險套匯
一、參考例句：
Dont risk your health.
不要拿你的健康冒險。
Patients are at risk here.
病人在這里是有風險的。
Those patients were at risk.
那些病人很危急。
They avoid risk and discomfort.
避開風險、追求安逸。
Another risk is immune rejection.
另一個危險就是免疫排斥。
Remember to Calculate Risk Carefully.
記住要仔細估算風險。
Jobs least at risk of being replaced.
最不可能被替代的職業。
The risk of pandemic influenza is serious.
全球流感的危險性非常嚴重。
To risk looking like a fool.
請像傻瓜一樣去冒險吧。
Is beta index the appropriate risk measurement?
貝塔系數是風險的正確度量嗎?
so far用法歸納
1. 表示“到如此之距離”，可視為far的加強說明，此時可根據情況選用時態。如：
My feet are very sore from walking so far. 走了這么遠的路，我的腳非常痛。
My mother lives so far away that we hardly ever see her. 我母親住得那么遠，我們很少見到她。
2. 表示“到如此之程度或范圍”，根據情況選用適當時態。如：
I can only help him so far. 我只能幫他到這種程度。
Was it wise to push things so far? 把事情弄到這種地步明智嗎?
3. 表示“到目前為止”“至今”(=until now)，注意它所連用的時態：
(1) 若強調so far所描述的謂語動作一直持續到現在，則謂語動詞用現在完成時。如：
So far there has been no bad news. 到現在為止還沒有什么壞消息。
So far 50 people have died in the fighting. 到現在為止，已有50人在戰斗中喪生。
有時還可修飾句中的非謂語動作，雖然此時的謂語不一定要使用現在完成時，但其中的非謂語動詞通常可視一個完成時態簡化而來的，如下面一句中的非謂語動詞found可視為which have been found之省略：
It is one of the funniest things found on the Internet so far this year. 這是今年互聯網上發現的最有意思的事情之一。
(2) 若不強調so far所描述的謂語動作一直持續到現在，則只是側重描述一種客觀現象，則可用一般現在時(謂語動詞通常為某些狀態動詞)。如：
So far, it is only talk. 至今還只是空談。
This is likely to be the biggest conference so far. 這很可能是迄今為止規模最大的一次會議了。
(3) 若so far并非描述謂語動作，而是間接地說明句中的某個名詞，此時的句子謂語需要具體語境來使用時態。如：
She gave us a brief resume of the project so far. 她給了我們一份該項目迄今為止的歷程簡介。(句中的so far間接地修飾名詞the project)
The new prime minister is facing his toughest political test so far. 新首相正面臨迄今為止最為嚴峻的政治考驗。(句中的so far間接地修飾名詞his toughest political test)
(4) 有時用于省略句，句子時態被隱含在語境中。如：
So far, so good. 到目前為止，一切順利。
bother的用法
一、bother的動詞用法
bother當及物動詞講時，有如下用法：
1. 使煩惱，使惱怒;使焦慮;打擾，糾纏，麻煩：
例句：Her baby sister bother her for candy.
她的小妹妹纏著她要糖果。
2. 迷惑，使…變糊涂，把…弄糊涂;使緊張不安，使慌張，使擔心：
例句：Her inability to understand the problem bothered her.
她對這問題不能理解使她迷惑不已。
3. 困擾，煩擾：
例句：Her sore foot bothers her.
她的痛腳困擾著她。
bother作為不及物動詞講時，用法如下：
1. [通常用于否定句]麻煩，費心，煩心，操心;盡力：
例句：Dont bother to get up.
請別起來。
2. 煩惱;擔心;焦急：
例句：Dont bother about me.
別為我擔憂。
二、bother 的名詞用法：bother作名詞講時，主要有兩個意思：一個是“麻煩”;另外一個是“煩惱”。
例句：Its no bother. What can I do for you?
不麻煩。我能為你做什么呢?
注意事項：
(1)在口語中說 don’t bother(…), 主要用于謝絕對方主動提出的善意幫助，一般翻譯為“不用費心(……)了”“不用麻煩(……)了”。
(2) can’t be bothered (to do sth) 是一個慣用句式，翻譯為“嫌麻煩而不做某事”“偷懶”。
bother的動詞用法和名詞用法就是這么多，同學們只要理解了例句，并且記住了例句，我想這個單詞也就算掌握了。
die of與die from有何區別
表示死的原因，其后通常接介詞of或from, 其區別大致為：
(1) 若死因存在于人體之上或之內(主要指疾病、衰老等自身的原因)，一般用介詞 of。如：die of illness (heart trouble, cancer, a fever, etc) 死于疾病(心臟病、癌癥、發燒等)
(2) 若死因不是存在人體之內或之上，而是由環境造成的(主要指事故等方面的外部原因)，一般用介詞 from。如：
die from an earthquake (a traffic accident, a lightning, a stroke, etc) 死于地震(交通事故、雷擊等)
(3) 若死因是環境影響到體內，即兩方面共有的原因，則可用 of, from 均可。如：
die of [from] a drink ( a wound, overwork, starvation, hunger and cold, etc) 死于飲酒(受傷、勞累過度、饑餓、饑寒等)
但是在實際運用中，兩者混用的情況較多。
sure與certain的用法與區別
一、兩者在用法上的相同點
兩者都可用作表語，表示“一定”或“確信”，有時可互換。互換的場合應注意以下幾點用法：
1. 表示說話者的態度或看法
即表示說話者自己的態度或看法，其意為“一定會”“肯定會”。此時主要用法有：
(1) 單獨用作表語。如：
One thing was sure [certain]: theyd be late. 有一件事是確定無疑的，他們會遲到。
(2) 后接不定式。如：
Hes certain [sure] to win. 他一定會成功。
Theyre certain [sure] to need help. 他們肯定需要幫助。
If you do this, you are certain [sure] to be found out. 如果你這樣做一定會被發現。
2. 表示句子主語的判斷或信念
即表示句子主語對某一情況的判斷或信念，其意為“相信”“確信”“有把握”等。此時通常用于以下結構：
(1) 后接of [about] sth。如：
He is certain [sure] of success. 他確信會成功。
Are you certain of [about] that? 你對此有把握嗎?
(2) 后接 of doing sth。如：
Our team is certain [sure] of winning. 我們隊有把握贏。
You can be sure [certain] of his agreeing. 你可以放心他會同意。
比較同義句：
He is certain [sure] of winning.
=He is sure [certain] that he will win. 他自信會贏。
(3) 后接 that / whether / if 從句。如：
I am sure [certain] that he is honest. 我肯定他是誠實的。
Im sure [certain] that its not your fault. 我敢肯定這不是你的錯。
Are you certain [sure] that this is the right road? 你肯定這條路對嗎?
注：當be sure [certain]為肯定式或疑問式時，后接that從句;當be sure [certain]為否定式時，后接whether [if]從句。如：
Im not sure [certain] whether he still works there. 我不能肯定他是否還在那里工作。
I wasnt sure [certain] whether he would agree. 我不太肯定他是否會同意。
He wasnt sure [certain] whether he would be able to get back in time. 他不能肯定他是否能準時回來。
(4) 后接其他從句。如：
Im not sure [certain] where she lives. 我不能肯定她住在哪里。
Im not certain [sure] who wrote it. 我不太清楚這是誰寫的。
二、只能用certain的情形
以下情況通常只用certain，而不用sure：
1. 當句中用了形式主語或形式賓語 it 時。如：
Its certain that hell come tomorrow. 他明天肯定會來。
I thought it certain that he would be late. 我肯定他會遲到。
Its certain that prices will go up. 價格肯定會上漲。
Its not certain how much it will cost. 這要花多少錢還不確定。
2. 當表示“某一”“某些”時。如：
A certain Mr Green wants to see you. 有個叫格林先生的人想見你。
Certain plants are good to eat but others are not. 有些植物好吃, 而其他一些則不好吃。
三、只能用sure的情形
在 Be sure (not) to do sth.(一定要或不要做某事)這類祈使句中通常不用 certain。如：
Be sure not to forget it. 千萬別忘記啦。
Be sure to get there before nine. 務必在九點前到達。
Be sure to turn off the light when leave. 離開時一定要關燈。
四、兩者在習語中的用法
1. 用于 for certain / for sure, 意為“肯定地”“確切地”等，兩者可互換。如：
I cant say for certain [sure] when he will come. 我不敢肯定地說他什么時候來。
risk的用法和辨析（文庫搜索）
risk的用法(5)
risk的用法和辨析
今天給大家帶來risk的用法,我們一起來學習吧，下面就和大家分享，來欣賞一下吧。
詞匯精選：risk的用法和辨析
一、詳細釋義：
n.
危險，風險(+of) [U,C]
例句：
Was she not stupid to take this risk?
她冒這樣的風險豈不是太愚蠢了?
例句：
Sometimes he went after these herbs at the risk of his life.
有時他得冒生命危險來找這些草藥。
(保險業承擔的)險;危險率;保險金額;保險對象 [C]
例句：
A group of firms may from a syndicate to pool the risk and assure successful distribution of the issue.
這樣的集團公司可能來自一個財團，實行風險共擔，確保股票成功發行。
貸款對象;賒銷對象 [C]
例句：
Before providing the cash, they will have to decide whether you are a good or bad risk.
在提供資金之前，他們得確認你是不是信譽良好的貸款對象。
v.
冒...的風險(+v-ing)，使遭受危險，以...作為賭注 [T]
例句：
Dont risk your health.
不要拿你的健康冒險。
例句：
The boatman was willing to risk ferrying them across.
船夫愿冒險渡他們過江。
冒險干(+v-ing) [T]
例句：
Dont risk disassembling the machine yourself.
不要冒險自己去拆卸機器。
二、詞義辨析：
danger,risk,hazard,menace,peril,threat
這些名詞均含有“危險、威脅”之意。 danger含義廣，普通用詞，指能夠造成傷害、損害或不利的任何情況。 risk指有可能發生的危險，尤指主動進行某種活動或去碰運氣而冒的危險。 hazard比risk正式，多指偶然發生的或無法控制的危險，常含較嚴重或有一定風險的意味。 menace所指的危險性最嚴重，表示使用暴力或造成破壞性的可能。 peril指迫在眉睫很有可能發生的嚴重危險。 threat普通用詞，語氣弱于menace，指任何公開侵犯對方的言行，給對方構成危險或威脅。
三、詞義辨析：
venture,chance,dare,hazard,risk
這些動詞均含有“敢于冒險”之意。 venture指冒風險試一試，或指有禮貌的反抗或反對。 chance指碰運氣、冒風險試試。 dare可與venture換用，但語氣較強，著重挑戰或違抗。 hazard主要指冒險作出某個選擇，隱含碰運氣意味。 risk指不顧個人安危去干某事，側重主動承擔風險。
四、相關短語：
risk capital
n.[經]風險資本(指為投機性商業投資提供的資金)
run a risk
冒險,擔風險
risk arbitrage
風險套匯
一、參考例句：
Dont risk your health.
不要拿你的健康冒險。
Patients are at risk here.
病人在這里是有風險的。
Those patients were at risk.
那些病人很危急。
They avoid risk and discomfort.
避開風險、追求安逸。
Another risk is immune rejection.
另一個危險就是免疫排斥。
Remember to Calculate Risk Carefully.
記住要仔細估算風險。
Jobs least at risk of being replaced.
最不可能被替代的職業。
The risk of pandemic influenza is serious.
全球流感的危險性非常嚴重。
To risk looking like a fool.
請像傻瓜一樣去冒險吧。
Is beta index the appropriate risk measurement?
貝塔系數是風險的正確度量嗎?
so far用法歸納
1. 表示“到如此之距離”，可視為far的加強說明，此時可根據情況選用時態。如：
My feet are very sore from walking so far. 走了這么遠的路，我的腳非常痛。
My mother lives so far away that we hardly ever see her. 我母親住得那么遠，我們很少見到她。
2. 表示“到如此之程度或范圍”，根據情況選用適當時態。如：
I can only help him so far. 我只能幫他到這種程度。
Was it wise to push things so far? 把事情弄到這種地步明智嗎?
3. 表示“到目前為止”“至今”(=until now)，注意它所連用的時態：
(1) 若強調so far所描述的謂語動作一直持續到現在，則謂語動詞用現在完成時。如：
So far there has been no bad news. 到現在為止還沒有什么壞消息。
So far 50 people have died in the fighting. 到現在為止，已有50人在戰斗中喪生。
有時還可修飾句中的非謂語動作，雖然此時的謂語不一定要使用現在完成時，但其中的非謂語動詞通常可視一個完成時態簡化而來的，如下面一句中的非謂語動詞found可視為which have been found之省略：
It is one of the funniest things found on the Internet so far this year. 這是今年互聯網上發現的最有意思的事情之一。
(2) 若不強調so far所描述的謂語動作一直持續到現在，則只是側重描述一種客觀現象，則可用一般現在時(謂語動詞通常為某些狀態動詞)。如：
So far, it is only talk. 至今還只是空談。
This is likely to be the biggest conference so far. 這很可能是迄今為止規模最大的一次會議了。
(3) 若so far并非描述謂語動作，而是間接地說明句中的某個名詞，此時的句子謂語需要具體語境來使用時態。如：
She gave us a brief resume of the project so far. 她給了我們一份該項目迄今為止的歷程簡介。(句中的so far間接地修飾名詞the project)
The new prime minister is facing his toughest political test so far. 新首相正面臨迄今為止最為嚴峻的政治考驗。(句中的so far間接地修飾名詞his toughest political test)
(4) 有時用于省略句，句子時態被隱含在語境中。如：
So far, so good. 到目前為止，一切順利。
bother的用法
一、bother的動詞用法
bother當及物動詞講時，有如下用法：
1. 使煩惱，使惱怒;使焦慮;打擾，糾纏，麻煩：
例句：Her baby sister bother her for candy.
她的小妹妹纏著她要糖果。
2. 迷惑，使…變糊涂，把…弄糊涂;使緊張不安，使慌張，使擔心：
例句：Her inability to understand the problem bothered her.
她對這問題不能理解使她迷惑不已。
3. 困擾，煩擾：
例句：Her sore foot bothers her.
她的痛腳困擾著她。
bother作為不及物動詞講時，用法如下：
1. [通常用于否定句]麻煩，費心，煩心，操心;盡力：
例句：Dont bother to get up.
請別起來。
2. 煩惱;擔心;焦急：
例句：Dont bother about me.
別為我擔憂。
二、bother 的名詞用法：bother作名詞講時，主要有兩個意思：一個是“麻煩”;另外一個是“煩惱”。
例句：Its no bother. What can I do for you?
不麻煩。我能為你做什么呢?
注意事項：
(1)在口語中說 don’t bother(…), 主要用于謝絕對方主動提出的善意幫助，一般翻譯為“不用費心(……)了”“不用麻煩(……)了”。
(2) can’t be bothered (to do sth) 是一個慣用句式，翻譯為“嫌麻煩而不做某事”“偷懶”。
bother的動詞用法和名詞用法就是這么多，同學們只要理解了例句，并且記住了例句，我想這個單詞也就算掌握了。
die of與die from有何區別
表示死的原因，其后通常接介詞of或from, 其區別大致為：
(1) 若死因存在于人體之上或之內(主要指疾病、衰老等自身的原因)，一般用介詞 of。如：die of illness (heart trouble, cancer, a fever, etc) 死于疾病(心臟病、癌癥、發燒等)
(2) 若死因不是存在人體之內或之上，而是由環境造成的(主要指事故等方面的外部原因)，一般用介詞 from。如：
die from an earthquake (a traffic accident, a lightning, a stroke, etc) 死于地震(交通事故、雷擊等)
(3) 若死因是環境影響到體內，即兩方面共有的原因，則可用 of, from 均可。如：
die of [from] a drink ( a wound, overwork, starvation, hunger and cold, etc) 死于飲酒(受傷、勞累過度、饑餓、饑寒等)
但是在實際運用中，兩者混用的情況較多。
sure與certain的用法與區別
一、兩者在用法上的相同點
兩者都可用作表語，表示“一定”或“確信”，有時可互換。互換的場合應注意以下幾點用法：
1. 表示說話者的態度或看法
即表示說話者自己的態度或看法，其意為“一定會”“肯定會”。此時主要用法有：
(1) 單獨用作表語。如：
One thing was sure [certain]: theyd be late. 有一件事是確定無疑的，他們會遲到。
(2) 后接不定式。如：
Hes certain [sure] to win. 他一定會成功。
Theyre certain [sure] to need help. 他們肯定需要幫助。
If you do this, you are certain [sure] to be found out. 如果你這樣做一定會被發現。
2. 表示句子主語的判斷或信念
即表示句子主語對某一情況的判斷或信念，其意為“相信”“確信”“有把握”等。此時通常用于以下結構：
(1) 后接of [about] sth。如：
He is certain [sure] of success. 他確信會成功。
Are you certain of [about] that? 你對此有把握嗎?
(2) 后接 of doing sth。如：
Our team is certain [sure] of winning. 我們隊有把握贏。
You can be sure [certain] of his agreeing. 你可以放心他會同意。
比較同義句：
He is certain [sure] of winning.
=He is sure [certain] that he will win. 他自信會贏。
(3) 后接 that / whether / if 從句。如：
I am sure [certain] that he is honest. 我肯定他是誠實的。
Im sure [certain] that its not your fault. 我敢肯定這不是你的錯。
Are you certain [sure] that this is the right road? 你肯定這條路對嗎?
注：當be sure [certain]為肯定式或疑問式時，后接that從句;當be sure [certain]為否定式時，后接whether [if]從句。如：
Im not sure [certain] whether he still works there. 我不能肯定他是否還在那里工作。
I wasnt sure [certain] whether he would agree. 我不太肯定他是否會同意。
He wasnt sure [certain] whether he would be able to get back in time. 他不能肯定他是否能準時回來。
(4) 后接其他從句。如：
Im not sure [certain] where she lives. 我不能肯定她住在哪里。
Im not certain [sure] who wrote it. 我不太清楚這是誰寫的。
二、只能用certain的情形
以下情況通常只用certain，而不用sure：
1. 當句中用了形式主語或形式賓語 it 時。如：
Its certain that hell come tomorrow. 他明天肯定會來。
I thought it certain that he would be late. 我肯定他會遲到。
Its certain that prices will go up. 價格肯定會上漲。
Its not certain how much it will cost. 這要花多少錢還不確定。
2. 當表示“某一”“某些”時。如：
A certain Mr Green wants to see you. 有個叫格林先生的人想見你。
Certain plants are good to eat but others are not. 有些植物好吃, 而其他一些則不好吃。
三、只能用sure的情形
在 Be sure (not) to do sth.(一定要或不要做某事)這類祈使句中通常不用 certain。如：
Be sure not to forget it. 千萬別忘記啦。
Be sure to get there before nine. 務必在九點前到達。
Be sure to turn off the light when leave. 離開時一定要關燈。
四、兩者在習語中的用法
1. 用于 for certain / for sure, 意為“肯定地”“確切地”等，兩者可互換。如：
I cant say for certain [sure] when he will come. 我不敢肯定地說他什么時候來。
risk的用法和辨析（文庫搜索）
